Recent developments in generative artificial intelligence have brought forth novel and practical applications of artificial intelligence using advanced machine learning and deep learning models, natural language processing and large language models, and artificial neural networks. One of the most notable AI applications is AI agents and one of their most prominent examples is AutoGPT and its subsequent iterations and versions called AutoGPTs.
The Rise of AI Agents: Definitions, Applications, and Limitations of AutoGPTs
Definition and Description
An AutoGPT is an AI application that uses the Generative Pre-Trained Transformer or GPT family of large language models to perform a particular task or set of tasks without the need for a user to use different prompts in each action or process. These applications are examples of AI agents. They are capable of performing and accomplishing autonomous tasks
The first two applications were posted on GitHub. These were AutoGPT, developed by an independent software developer called Significant Gravita and released on 30 March 2023, and BabyAGI, developed by venture capitalist Yohei Nakajima and released on 3 April 2023.
AutoGPT is an experimental project aimed at making GPT fully autonomous using the GPT-4 large language model. BabyAGI is a Phyton script based on the pared-down version of the original Task-Driven Autonomous Agent that uses APIs from OpenAI and Pinecone.
Other iterations and versions have since emerged. These applications are collectively called AutoGPTs. One example is AgentGPT. It was developed by software engineers Asim Shrestha and Adam Watkins using the Next.JS React framework for web applications, the LangChain framework built around large language models, and the GPT-3 large language model.
AgentGPT was released to the public on 9 April 2023. The application enables a user to give the AI agent a specified goal. The agent will then create and execute a plan. It uses large language models to generate a task list and then execute each task with reiterations.
The applications above can create, prioritize, and execute tasks. Tasks are created based on the result of previous tasks and a predefined objective. They also have internet access for information gathering and long-term and short-term memory.
Nevertheless, based on the aforementioned, AutoGPTs are both generative AI applications and AI agent examples. Their core function is similar to generative applications such as ChatGPT but they are also considered AI agents because they behave autonomously without the need for a user to enter prompts at each step of an action or process.
Advantages and Disadvantage
The core advantage of AutoGPTs rests on the fact that they are autonomous generative AI applications designed to generate and perform tasks with minimal to zero human input and intervention through self-prompting. The following are the specific advantages:
• Full Automation of Tasks: One of the key advantages of AutoGPTs is that they can be used for task automation. The tasks that these applications can perform will depend on the user-defined objective and application capabilities.
• Expansive Applications: The use cases of task automation are expansive. These include completing the entire content creation process, going through the stages of software development, and running an online-enabled business, among others
• Access to the Internet: Several applications can access the internet for performing web searches and gathering information. This is helpful for generating and performing tasks that require reference to a knowledge base.
• Thinking and Reasoning: The self-prompting ability of these applications, in addition to their use of large language models and access to the internet, allows them to demonstrate autonomous thinking capabilities, including reasoning skills.
• Long-To-Short-Term Memory: Another advantage of AutoGPTs is their long-term and short-term memory management. This equips artificial neural networks with the ability to learn and improve from previous experiences.
Most AutoGPTs are in their earlier phases. Their capabilities remain limited. However, this is not the main disadvantage. These applications share the same disadvantages as generative AI and AI in general. Below are the specific disadvantages or issues and limitations:
• Somewhat Expensive: Self-prompting AI applications can be quite expensive to run. The specific AutoGPT application from Significant Gravita requires an API key from OpenAI and running it on backroad consumes credits.
• Performance Limitations: These applications are not perfect. Experimental applications are in their earlier stages. It is possible for these applications to not perform well in complex and real-world situations. Monitoring and auditing are still required.
• Inaccuracies and Biases: Another disadvantage of AutoGPTs is their likelihood to generate outputs that are inaccurate or biased. It is also possible for them to perform tasks that can have ethical and serious legal implications.
• Disclaimer on Liabilities: Developers also have an expressed disclaimer that frees them from whatever responsibilities or liabilities for whatever losses or damages that may occur resulting from the use of these applications.