Generative AI For The Rest Of US

Building on the Foundations: Platforms, Agents, Autonomous Systems, and Beyond

Developers today are finding it increasingly easy to use Generative AI, which is creating a thriving ecosystem of platforms, agents, and autonomous systems. We are going to explore some of them; however, this list is by no means exhaustive. The goal is to provide a glimpse of the current possibilities that Generative AI offers.

Autonomous Agent-Driven Solutions

The concept of autonomous agents is not new. Examples implementing this technology can be found in various fields, such as robotics, finance, and video games.

In robotics, for instance, autonomous agents are used to control robots and make decisions based on their environment. Amazon warehouses are a very good example. The e-commerce giant employs autonomous robots like Proteus and Cardinal. Both perform everyday complex tasks with a surprising level of efficiency that sets a new standard in warehouse automation. Proteus operates safely among employees, while Cardinal sorts heavy packages. After acquiring Kiva Systems in May 2012, which was rebranded as Amazon Robotics, Amazon now has more than 520,000 robotic units in operation in its various centers. The integration of autonomous robots into Amazon's operations exemplifies the broader potential of these machines to function as autonomous agents across other industries. Another advanced robotics company is Boston Dynamics, which has developed robots like Spot, Atlas, and Handle, capable of performing extraordinary tasks. Their BigDog robot, a quadruped dog-like robot, was developed for the US military to carry heavy loads across rough terrains and serve as a pack mule for soldiers. It was indeed funded by DARPA, the Defense Advanced Research Projects Agency. If you don't know Boston Dynamics robots yet, you should definitely watch some videos demonstrating their capabilities on YouTube. You'll be amazed, I promise!

Autonomous agents, by definition, are systems capable of independent actions in dynamic and unpredictable environments. They make decisions and perform tasks without continuous human guidance, using data from their surroundings to navigate and fulfill their objectives. Self-governance, adaptability, and the capability to make real-time decisions characterize autonomous agents, not just in robots but in any other system that we qualify as autonomous. In fields like agriculture, autonomous agents could autonomously navigate fields, harvesting or planting based on real-time environmental conditions. In video games, they could enhance the overall playing experience by providing a more realistic opponent, a teammate, or even a non-player character (NPC). The application domains for autonomous agents are vast: Wherever there is a need for a system to operate independently and take actions based on its environment to achieve a goal, autonomous agents can be deployed.

"[Intelligent] autonomous agents are the natural endpoint of automation in general. In principle, an agent could be used to automate any other process. Once these agents become highly sophisticated and reliable, it is easy to imagine an exponential growth in automation across fields and industries." ~ Bojan Tunguz, Machine Learning Engineer at Nvidia

As a Matrix fan, it's fascinating to see the concept of autonomous agents perfectly illustrated in the movie. Smith, the main antagonist, is an extremely powerful and likely the most advanced autonomous agent in the Matrix. He can replicate himself, take control of other entities, and make decisions independently. He was so independent and so equipped with the ability to learn and adapt that he even went rogue, breaking free from the system's control and becoming a threat to both humans and machines.

★ What is the Matrix? Control. The Matrix is a computer-generated dream world built to keep us under control in order to change a human being into this. [holds up a Duracell battery] ~ Morpheus

Perhaps one day, we will be able to create a Matrix-like simulation with "conscious" autonomous agents. The concept of generative agents, designed to mimic genuine human behavior, was introduced in some studies. For instance, the authors of the paper "Generative Agents: Interactive Simulacra of Human Behavior" demonstrated how agents can store, retrieve, and synthesize their plans, memories, and experiences in a plain language manner by incorporating an LLM. Similar to the video game "The Sims," the agents operate in an interactive sandbox setting where they can engage in conversation. When these generative agents were observed in groups, the study found that they exhibited both sophisticated social interactions and believable individual behaviors. The environment in which the agents were placed was a simple text-based world. It's far from the complexity of the Matrix, but it's a small step in the same direction.

In the following sections, we will explore some platforms for creating AI agents and autonomous systems. These platforms are still in the experimental phase. They are not yet as efficient as the autonomous robots in Amazon's warehouses, but they represent a significant step forward in the development of these technologies.

AutoGPT: A Foundation for Making AI Agents Accessible

AutoGPT is designed to democratize access to artificial intelligence, particularly focusing on the creation, utilization, and management of AI agents. It achieves this by offering a suite of tools that simplify the process of building, testing, and managing agents, making it more approachable for a wide range of users, regardless of their technical expertise. Deploying AutoGPT involves following a series of steps to set up the environment, configure the AI agent, and interact with it.

AutoGPT provides users with resources to create and evaluate AI agents efficiently. These resources include frameworks and standardized tests that assist in developing agents that are both effective and reliable. AutoGPT uses the Agent Protocol standard to ensure that the AI agents developed with its tools remain compatible with both current and future applications. This forward-thinking approach guarantees that agents can evolve without losing interoperability.

AutoGPT is designed to streamline complex tasks by decomposing them into smaller, manageable sub-tasks, which are autonomously executed in a sequence to accomplish the overarching goal defined by the initial user input. A key characteristic of AutoGPT is its ability to fetch data from the internet and process it to generate up-to-date outputs. This capability allows AutoGPT to teach itself and adapt to new challenges. Furthermore, AutoGPT incorporates a short-term memory feature for the task at hand, providing the necessary context for the ensuing sub-tasks to fulfill the broader objective. Being multimodal, AutoGPT accepts both textual and visual inputs.

AutoGPT works by breaking down complex tasks into smaller sub-tasks, which are then executed autonomously in a sequence to achieve the overall goal, such as automating workflows, conducting data analysis, or generating innovative recommendations. Here is the detailed step-by-step process:

1. Initial User Configuration:

Users initiate the process by inputting some initial details: the AI's designated name, its functional role, and a set of goals, capped at five. For instance, an AI named PhoneMarketGPT could be tasked with conducting market research on smartphones, extracting a top-five list of pros and cons, ordering them by price, summarizing customer reviews, and concluding with a recommendation as a final step.

2. Task Origination Agent:

After receiving the user's input, this agent interprets the objectives, formulates a list of tasks, and outlines the steps necessary for their completion. The generated tasks are then relayed to the task prioritization agent.

3. Task Prioritization Agent:

This agent evaluates and organizes the tasks, ensuring a logical sequence is established. This step is critical to avoid any operational deadlocks where a pending task relies on the outcome of a yet-to-be-executed task.

4. Task Execution Agent:

The task execution agent is responsible for carrying out the tasks in the designated order. It autonomously interacts with external resources like GPT-4, the internet (for data retrieval), and the file system (for data storage).

5. Inter-Agent Dialogue:

The agents are designed to collaborate, exchanging information to refine the process toward achieving the user-specified goals. If results are unsuitable, the task origination agent can be prompted to reassess and produce a revised task list. This dynamic and iterative process repeats until the goals are met.

6. Result Presentation:

The culmination of the agents' activities is presented to the user through several categories:

• Thoughts: Each agent shares insights gained upon action completion.
• Reasoning: The agents provide justifications for their selected strategies.
• Plan: A revised task list is presented if necessary.
• Critique: An evaluation of the agents' performance is shared with the user at the end.

"Auto-GPT illustrates the power and unknown risks of Generative AI. For enterprises, it is especially important to include a human in the loop approach when developing and using Generative AI technologies like Auto-GPT." ~ Clara Shih, CEO of Salesforce’s Service Cloud

Auto-GPT, despite its innovative approach to automating complex tasks, encounters some limitations such as hallucinations, inconsistencies, and the need for continuous OpenAI API calls, which can be costly. Each task Auto-GPT undertakes involves multiple GPT-4 API requests. For a long sequence of reasoning, feedback, and adjustments, costs can accumulate rapidly. Moreover, Auto-GPT sometimes finds itself caught in infinite loops.

Through its leaderboard, which allows agents like evo.ninja to be evaluated according to their performance, the platform offers a competitive element and encourages a community-driven approach to enhancing AI agent capabilities.

AgentGPT: Configure and Deploy Autonomous AI Agents

AgentGPT is another open-source platform that enables users to configure and deploy autonomous AI agents. These AI agents can be customized by naming them and setting specific goals for them to accomplish. The AI will autonomously generate tasks, execute them, and learn from the outcomes to achieve the predefined goals. Like AutoGPT, AgentGPT uses a process where complex tasks are split into smaller sub-tasks, which are then executed in sequence to achieve the desired outcome. In the future, the AgentGPT roadmap includes interaction with websites and people, and a cross-agent communication system. Deploying AgentGPT locally on your machine requires some technical skills. However, once deployed, it can be used by non-technical users to interact with the AI agent since it's accessible through a web interface. A demo is available on agentgpt.reworkd.ai where you should use your OpenAI API key to use it.

AutoGPT vs. AgentGPT

AutoGPT and AgentGPT are the most popular platforms for AI agents. Although they are both designed to harness the power of AI for automating tasks, they cater to different user bases and offer distinct features.

AutoGPT is designed for developers and those with technical skills, providing a comprehensive toolkit for creating, managing, and evaluating AI agents. It's highly customizable and suited for users looking to delve into the mechanics of AI agents. Its reliance on the Agent Protocol standard ensures compatibility and future-proofing of AI agents across various applications.

AgentGPT, on the other hand, aims to be more accessible to non-technical users through a user-friendly web interface. It allows for the configuration and deployment of autonomous AI agents without deep technical knowledge. While it offers ease of use and accessibility, it sacrifices some of the customizability and depth available in AutoGPT.

In summary, AutoGPT is the choice for those wanting to build and tailor AI agents from the ground up, and AgentGPT is designed for users seeking a more straightforward approach to deploying AI agents.

Multi-GPT

Multi-GPT on GitHub presents a similar approach to task execution through the collaboration of specialized "expertGPTs." This system allows each agent, equipped with both short and long-term memory, to communicate and collaborate on tasks. The Multi-GPT system supports a wide array of functionalities, including task assignment, internet research capabilities, memory management, and integration with GPT-4 for text output and GPT-3.5 for content summarization. Users looking to leverage Multi-GPT may enhance their setup with optional memory backends like Pinecone and Milvus, two vector databases specifically designed for LLMs and similar applications. The setup process involves cloning the repository, installing dependencies, and configuring the .env file with the necessary API keys. Therefore, a basic understanding of Python and other related technologies is recommended for users interested in deploying Multi-GPT.

BabyAGI

BabyAGI on GitHub is a Python script that serves as an AI-driven task management solution. It leverages OpenAI's capabilities and vector databases to manage and execute tasks dynamically. The system's workflow involves task retrieval, execution via OpenAI's API, result storage, and the generation and prioritization of subsequent tasks. BabyAGI utilizes Chroma/Weaviate for efficient data handling. The project's simplicity makes it an excellent starting point for developers interested in task-oriented AI applications. BabyAGI has a continuous mode that allows it to run indefinitely, executing tasks as they are generated. It's worth noting, however, that continuous operation might lead to significant API usage and higher costs as a result.

Multi-Model Language Model Interfaces

The concept is straightforward: having access to multiple large language models through the same interface. Poe, a tool developed by Quora, allows users to interact with several large language models from a single interface. Switching between models is as simple as clicking a button, enabling users to compare the outputs of different models and select the one that best suits their needs. Poe supports GPT models from OpenAI, Claude, LlaMA, and Mistral, among others.

ChatGPT GPTs

GPTs can be defined as plugins or custom versions of GPT that have been tailored for a specific task or domain using specific prompts, data, and integrations with external platforms. They can be created by companies or individuals to enhance the capabilities of ChatGPT, and even if you don't have coding skills, it's possible to create your own. They can be shared publicly or used internally within a company. Until they were made available to all developers and creators, the initial GPTs were only accessible to select companies. Expedia, FiscalNote, Instacart, KAYAK, Klarna, Milo, OpenTable, Shopify, Slack, Speak, Wolfram, Zapier, etc. were among the first companies to develop a GPT. These companies have contributed to enriching the possibilities offered by ChatGPT through their platforms. Now, creating GPTs is publicly available, and the number of GPTs is multiplying. They can be categorized into the following types:

• Research plugins: These enable web searches and the extraction of information, including facts, definitions, and real-time search results. "Consensus" is an example of a research plugin that allows users to search 200M academic papers from Consensus, get science-based answers, and draft content with accurate citations.
• Reasoning plugins: These enable calculations and the use of third-party services. "Code Copilot", for example, is a reasoning plugin that helps users write code by providing code snippets, explanations, and suggestions.
• Service plugins: These allow for specific tasks, such as planning trips or booking restaurants. An example of a service plugin is "Canva", which enables users to create designs and graphics directly within ChatGPT using the Canva API.
• Content plugins: These facilitate content creation, such as writing articles, emails, or social media posts. An example of a content plugin is "Humanizer Pro", which helps users humanize their content to bypass AI detectors.

AI-Powered Writing Platforms

Jasper

Jasper is an AI writing assistant designed to help produce high-quality content quickly, such as blog posts, social media updates, and marketing emails. It can write and understand over 30 languages, making it useful for a wide audience, including those looking to create or translate content. Jasper stands out for its extensive analysis of the internet to ensure the originality of its work and avoid plagiarism. It has been trained by marketing professionals in over 50 different skills, from writing email subject lines to creating stories. The platform also facilitates teamwork by offering features like individual logins, project management, and easy workspace transition. It offers plans tailored for individuals and businesses, and a free trial. Jasper includes several key features to improve the content creation process. It integrates with Grammarly, which automatically identifies and corrects grammatical, spelling, and clarity issues. It offers a voice input feature, allowing for the convenience of dictating content details and instructions. For those prioritizing digital visibility, Jasper's SEO capabilities automatically incorporate targeted keywords into content. They offer seamless integration with Surfer for streamlined SEO analysis. Additionally, Jasper can adjust wording and tone, ensuring content resonates with the intended audience by tailoring the message to match reader perceptions.

GrowthBar

GrowthBar