Introduction
This lesson aims to dive into the latest developments and trends in AI agents. We'll talk about popular AI agents and their fascinating features and explore the exciting possibilities they may hold for the future.
We start by discussing the previously mentioned AutoGPT experiment that pushes GPT-4 towards full autonomy which has gained notable attention and popularity, even outperforming well-established projects like PyTorch in terms of GitHub stars.
Next, we delve into the emergence of "Plan-and-Execute" agents that separate high-level planning from immediate execution and the ways these agents could be improved for better efficiency and performance.
Following that, we explore GPT-4's plug-in and code interpreter capabilities, which augment the model's abilities and potential uses, facilitating tasks like data analysis, visualization, and internet interaction. We also provide insights on how to access and use these plugins.
Lastly, we probe into the ongoing debate in AI about the efficiency of the "Small context window with a retriever approach" versus a "large context window without retrievers approach.” We'll examine each method's potential trade-offs and benefits, emphasizing the 100k tokens context window of the new Anthropic model.
AutoGPT
AutoGPT, an experimental open-source project aimed at making GPT-4 fully autonomous, has recently gained significant attention on GitHub, reaching 100k stars in less than three months. This surpasses the popularity of PyTorch, a widely used deep learning framework with 74k stars on GitHub. The rapid growth of AutoGPT's popularity can be attributed to its ability to inspire developers and enthusiasts. AutoGPT has been described as an experiment to test and understand the limits of GPT-4 (and 3.5) as a potential autonomous agent. While it may not be perfect yet, its capabilities are growing quickly.
There are differing opinions on AutoGPT's current usefulness. Some users believe it is overhyped and cannot truly "run a business autonomously.” Others argue that it is still experimental and that its potential will become more evident as it evolves.
AutoGPT's simplicity has been noted by some developers, who claim that the code is easy to understand compared to more complex projects. This simplicity has contributed to its rapid popularity on GitHub. AutoGPT's autonomous capabilities have raised concerns about potential misuse and the need for safeguards to prevent unethical activities.
Planning Agents
In the realm of "Plan-and-Execute" agents, the segregation of planning and execution is a step forward for agents able to solve more complex tasks. With strategies to enhance these agents, such as support for long sequences of steps and revisiting plans, we are looking at the future of sophisticated and dynamic AI systems.
This approach separates higher-level planning from immediate execution and consists of a planner and an executor.
The planner, typically a language model, uses its reasoning ability to devise a course of action and manage any ambiguities or edge cases. A parser can be appended at the end to translate the raw language model's output into a sequence of steps.
On the other hand, the executor is responsible for actualizing these high-level objectives. Given a single step, it discerns the necessary tools or actions to fulfill that step, which could be accomplished in single or multiple stages.
This architecture offers several advantages. By decoupling planning from execution, one language model can concentrate solely on planning, and another can focus on execution, enhancing reliability on both fronts. It also facilitates the replacement of these components with smaller, fine-tuned models in the future. However, the major drawback of this method is the increased number of calls to the language models. Still, due to the separation of concerns, these calls can potentially be made to smaller models, which would be faster and more cost-effective.
Moving forward, there are several ways to enhance the "Plan-and-Execute" agents. These include:
- Support for Long Sequences of Steps: Currently, only a few steps are handled.
- Revisiting Plans: Presently, planning only happens once, in the beginning, and is never revisited. However, there may be a need for a mechanism that allows for periodic revisiting and adjustment of the plan, either after each step or as necessary.
- Evaluation: Many of these enhancements are somewhat unbenchmarked. Therefore, more rigorous evaluation methods for agent frameworks are needed.
- Selection of Execution Chain: At present, only a single execution chain exists. However, it might be beneficial to have multiple execution chains, with the planner specifying which one to use based on the task at hand.
The ChatGPT Code Interpreter
GPT-4, OpenAI's latest iteration of its model, has introduced the use of plugins to extend its capabilities. Among these plugins, namely the ChatGPT Code Interpreter and the ChatGPT Web Browser, aim to augment GPT-4's abilities, enabling it to interact with the internet, conduct data analysis, visualizations, and file conversions.
As the AI's core training data extends only until September 2021, the generated text will contain information up to this point in time. The internet access plugin can bypass this constraint, allowing users to ask queries on recent events such as: "What was the outcome of the Celtics game last night?”
Another notable plugin offered by OpenAI is the Code Interpreter, which facilitates intricate computations using Python.
This plugin essentially acts as a proactive junior programmer, enhancing workflow efficiency. This plugin has been utilized for various tasks, such as visualizing lighthouses, performing basic video editing, and analyzing large datasets.
The blog post on the official OpenAI portal stated:
“We provide our models with a working Python interpreter in a sandboxed, firewalled execution environment and some ephemeral disk space. Code run by our interpreter plugin is evaluated in a persistent session that is alive for the duration of a chat conversation (with an upper-bound timeout), and subsequent calls can build on top of each other. We support uploading files to the current conversation workspace and downloading the results of your work.”
Accessing ChatGPT Code Interpreter
To access this plugin, users need to subscribe to ChatGPT Plus, and it is gradually being made available to all subscribers. Once you gain access, the plugin can be installed by navigating to the three-dot menu next to your login name at the bottom-left of the window, selecting the Beta features menu, and toggling on 'Plug-ins.’ If you wish for GPT-4 to access the internet as well, toggle on 'Web browsing.’ Then, under the language model selector, you can find the drop-down menu to select and install the Code Interpreter. With this plugin enabled, users have the option to interact with GPT-4 with enhanced capabilities.
ChatGPT Web Browser Plugin
The ChatGPT Web Browser plugin offers GPT-4 internet accessibility, enabling it to interact with web content. This functionality is particularly advantageous for tasks such as searching for information, browsing social media, or generating code snippets based on specific websites.
ChatGPT plugins fall into two categories: internal and external. Internal plugins are managed and hosted by OpenAI. This includes tools like the web browser and the code interpreter, which enhance the AI's capabilities. On the other hand, external plugins are built and provided by third-party entities.
The introduction of plugins such as the ChatGPT Code Interpreter and Web Browser significantly broadens the capabilities and potential uses of GPT-4. These tools allow GPT-4 to interact with the internet, perform tasks like data analysis and visualization, and access up-to-date information.
Plug-in Limitations
ChatGPT plugins, while introducing innovative features, also reveal some challenges and potential problems. A primary concern revolves around the centralization of power and influence, as these plugins could lead users to interact predominantly with ChatGPT, overshadowing individual websites and businesses.
There’s the risk of chatbots diverting web traffic and affecting revenue for industries across. For instance, while a certain travel planning plugin is useful, there could be instances where users might prefer to use the direct website since the plugin only presents a subset of results compared to the full site.
All businesses may not favor the plugin approach. It is noted that plugins pull users out of their app's experience. This sentiment could drive businesses to create their own AI services. For example, a popular online grocery service and a travel company are developing AI assistants, leveraging AI technology while keeping users within their platforms.
Anthropic Claude 100k token window
In the realm of large language model question-answering tasks, there's an ongoing debate about the necessity of a document retrieval stage, especially when using models with extensive context windows, specifically focusing on the Claude model developed by Anthropic, which is renowned for its sizeable 100k token context window.
Retrieval-based Architectures and Their Role
The process typically followed in question-answering tasks involves retrieval-based architectures. They work by sourcing relevant documents and using an LLM to convert the retrieved information into a response. The Claude model boasts a substantially larger context window compared to many other models.
The evaluation of new strategies brings to light the pivotal debate between the "Small context window with a retriever approach" and its counterpart, the "Large context window without retrievers’ approach.” The choice between these two becomes a significant point of consideration given the evolving trends in the industry.
- The Impact of Larger Context Windows: Larger context windows, such as Anthropic's 100k token context window, significantly enhance LLM functionality. With the ability to process and understand a broader range of text, the need for a retriever can be eliminated. However, this approach comes with limitations, including higher latency and potential reductions in accuracy as document length increases. This underlines the importance of considering each application's unique requirements and constraints.
- The Relevance of the Retriever-Based Approach: Despite advancements in larger context windows, the traditional approach of "small context window with a retriever architecture" still retains significant value. Retrievers can selectively present relevant documents for a specific question or task, maintaining high accuracy even when working with a large text corpus. In addition, retrievers can drastically reduce latency times compared to models without retrievers.
In scenarios where latency isn't a critical factor and the corpus is relatively small, retriever-less approaches could be a viable option, especially as LLM context windows continue to expand and models become quicker.
Both models have unique strengths and face different challenges. The selection between the two largely depends on the application's specific needs, such as the size of the text corpus, acceptable latency, and the required level of accuracy.
Conclusion
Our discussion explored the latest trends in agent-based technology, including popular agents and their applications. AutoGPT emerged as a standout, inspiring many with its autonomous capabilities. Equally noteworthy is the increasing use of Language Learning Models for planning in multi-agent architectures.
The growing trend of GPT-4 plugins, such as the browser and code interpreter plugins, emphasizes the role of customization in software development. We also delved into the nuances of context windows, with Anthropic's 100k tokens context window being a focal point.
The trends illustrate the rapid advancement in this field. Customization, evident in the rise of plugins for AI models, is becoming increasingly important. Additionally, discussions around context window sizes hint at the continuous pursuit of accuracy and computational efficiency in AI.
These insights signal an exciting future for AI, with these trends expected to shape the AI landscape significantly.
Congratulations for finishing the last module of the course! You can now test your new knowledge with the module quizzes.