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Chatgpt has created quite a buzz in the AI world. We are witnessing many other models with incremental improvements. But none of them focused on improving the interaction between humans and AI. You still need to give it a great push to get the desired results. This is where AutoGPT excels. He can “The call of self-government” and critically reviews his work. Are you interested to know about it? How does it work and what makes it unique? And perhaps most importantly, what are its limitations? Don’t worry, we’ve got you covered. Let’s discuss all these questions in this article. Join me to delve into the topic together.
AutoGPT is an open source program developed by Toran Bruce Richards (game developer and important founder of Gravitas). It uses GPT-3.5 or GPT-4 APIs to build fully autonomous AI agents. It stands out because you don’t have to drive the model based on your understanding. You simply provide a task along with a list of goals and it does the rest. Unlike ChatGPT, it can also access external resources to make decisions. Did you know that it received more stars than Pytorch (a famous open source ML library) within weeks of its release? Here’s a chart showing his stellar history.
Image generated by Stellar History
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AutoGPT combines the power of GPT-4 and a personal assistant to independently generate, execute, and prioritize tasks. As an autonomous system, it creates AI agents to perform specific tasks. These agents also communicate with each other. Here are the steps that describe how AutoGPT works:
Step 01: Input from user
First, the user must enter the following three inputs: AI name, AI role, and up to 5 goals. For example, I can create an AI called MarketResearchGPT and its role will be to conduct market analysis for various items. I can set goals such as doing market research for different phones, getting a list of the top five with their pros and cons, ranking them in ascending order of their prices, summarizing their customer reviews, and the process is over.
Step 02: Create Task Agent
After the user provides input, the task creation agent understands the goal, creates a list of tasks, and suggests steps to accomplish them. The resulting set of tasks is then passed to the task prioritization agent.
Step 03: Task Prioritization Agent
A task prioritization agent looks at the order of tasks to make sure it makes logical sense. Because we don’t want to get into a deadlock where our current task depends on the outcome of a task that hasn’t been executed yet.
Step 04: Task execution agent
The Task Execution Agent, as the name suggests, uses GPT-4, the Internet, and other resources to perform these tasks.
Step 05: Communication between agents
Agents can communicate with each other to achieve a user-defined goal. For example, if unsatisfactory results are generated, it can contact the Task Creation Agent to create a new list of tasks. Hence, it becomes an iterative process.
Step 06: The final result
The actions of these agents at the end of the user can be seen in the following form:
thoughts: The AI agent shares their thoughts after the action is complete
discuss: He explains his choices as to why he chooses a particular course of action
The plan: The plan includes a new set of tasks
Criticism: Critically review the selection by identifying limitations or concerns
It also uses external memory to track history and learn from its past experiences to produce more accurate results.
Although AutoGPT and ChatGPT are built on the same technology which is the GPT API, we can name some major differences which are as follows:
Access to real-time data
ChatGPT uses the latest GPT-4 model trained until September 2021, which means we cannot extract real-time information. AutoGPT has access to external resources and incorporates the latest trends in its responses.
autonomous functioning
Unlike ChatGPT, which requires constant prompting from the user, AutoGPT is autonomous in this regard and does not require constant prompting. It really helps generate ideas.
memory management
ChatGPT has memory constraints in the form of context windows for LLMs such as GPT-4, while AutoGPT uses vector databases and is suitable for both short- and long-term memory management.
Image and speech functions
ChatGPT is limited to text data, while you can create images and convert text to speech using AutoGPT.
You will need an OpenAI API key because AutoGPT is built on top of GPT. If you don’t have one, you can sign up for a free account to get some free credits. Follow the steps below to install AutoGPT on your local computer.
requirements
to install it
Clone the GitHub repository to your local directory using the following command:
git clone https://github.com/Significant-Gravitas/Auto-GPT.gitGo to the project directory using the following command:
Run the following command to download the required dependencies:
pip install -r requirements.txtLocate the “.env.template” file in your Auto-GPT folder. Please also check the hidden files if you can’t find them. Make a copy of this file:
Open the .env file and replace OPENAI_API_KEY with the key generated from your account. Save and close the .env file.
Run the command below to start AutoGPT:
And if you’re using GPT-3.5, then you can run:
python -m autogpt --gpt3onlyyou are good now go In case of any problems, please refer to the official documentation: Auto-GPT Setup
Although AutoGPT can generate content with minimal human intervention, it has several significant disadvantages, such as high costs, limited functionality, inadequate understanding of context, data bias, limited creativity, and security risks. It still cannot achieve AGI (Artificial General Intelligence) due to data quality, generalizability and explainability issues. Despite its shortcomings, it has enormous potential to revolutionize our daily lives and our work. I hope you enjoyed reading the article and let me know what you think about AutoGPT in the comments section.
Kanwal Mehreen is a software developer with a strong interest in data science and the application of artificial intelligence in medicine. Kanwal has been selected as a Google Generation Scholar 2022 for the APAC region. Kanwal enjoys sharing technical knowledge by writing articles on trending topics and is passionate about improving the representation of women in the tech industry.
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