What is Auto-GPT? Next-level AI tool surpassing ChatGPT?

Like many people, your mind may have been blown recently by the possibilities of ChatGPT and other big language models (LLMs) like the new Bing or Google’s Bard.

For anyone who somehow hasn’t come across them – which is probably unlikely since ChatGPT is the fastest growing app ever – here’s a quick recap:

LLMs are software algorithms trained on large text datasets, enabling them to understand and respond to human language in a very vivid way.

The best-known example is ChatGPT, a chatbot interface powered by GPT-4 LLM that has taken the world by storm. ChatGPT is able to communicate like a human and generate everything from blog posts, letters and emails to fiction, poetry and computer code.

They are impressive, so far, the LLM is a remarkably limited one. They are only able to complete one task, such as answering a question or generating a piece of text, before they require further human interaction (known as “prompts”).

This means that they are not always the best at more complex tasks that require multi-step instructions or depend on external variables.

Enter Auto-GPT – a technology that attempts to overcome this obstacle with a simple solution. Some believe it could even be the next step toward the “holy grail” of AI—the creation of general, or robust, AI.

Let’s take a look at what that means first:

Strong AI Vs. Weak AI

Current AI applications are typically designed to carry out a single task, getting better and better at it as they are fed more data. Some examples include analyzing images, translating languages, or navigating self-driving vehicles. Because of this, they are sometimes referred to as “special AI,” “narrow AI,” or “weak AI.”

A generalized AI is one that is theoretically capable of performing many different types of tasks, even those it was not originally designed to carry out, in the same way that a naturally intelligent entity (such as a human) could. It is sometimes called “strong AI” or “artificial general intelligence” (AGI).

AGI is probably what we traditionally thought of when we thought of what AI would look like in the early days of the previous decade before machine learning and deep learning made weak/compact AI an everyday reality. Think science fiction AI represented by robots like Data in Star Trek that can do anything a human can do.

So what is Auto-GPT?

The simplest way to look at it is that Auto-GPT is capable of carrying out more complex, multi-step processes than existing LLM-powered applications by creating its own prompts and feeding them in a loop.

Here’s one way to think about it: Getting the best results from an app like ChatGPT requires careful thought in the way you phrase the questions you ask. So why not let the app generate the question itself? And while he’s at it, also get to asking what the next step should be – and how it should go … and so on, creating a loop until the task is complete.

It works by breaking a large task into smaller sub-tasks and then spinning up independent auto-GPT instances to work on them. The original paradigm acts as a kind of “project manager”, coordinating all the work undertaken and integrating it into the finished result.

Using GPT-4, Auto-GPT is able to browse the Internet and incorporate the information it finds into its calculations and output, to construct sentences and prose based on the text it has studied. In this respect, it is more similar to the new GPT-4 enabled version of Microsoft’s Bing search engine. It also has better memory than ChatGPT, so it can create and remember longer chains of commands.

Auto-GPT is an open-source application that uses GPT-4 and was created by one person, Toran Bruce Richards. Richards said he was motivated to develop it because traditional AI models “while powerful, often struggle to adapt to tasks that require long-term planning, or are unable to autonomously refine their approaches based on real-time feedback. .”

It is one of a class of applications known as iterative AI agents because they have the ability to autonomously use the results they generate to generate new prompts, chaining these operations together to complete complex tasks.

Another such agent is BabyAGI, which was created by a partner at a venture capitalist firm to help him with day-to-day tasks that were too complex to research new technologies and companies like ChatGPT.

What are some applications of Auto-GPT and AI agents?

While applications such as ChatGPT have become famous for their ability to generate code, they are limited to relatively short and simple programming and software design. Auto-GPT, and potentially other AI agents that work similarly, can be used to develop software applications from start to finish.

Auto-GPT is also capable of helping businesses autonomously increase their net worth by examining their processes and providing intelligent recommendations and insights on how to improve them.

Unlike ChatGPT it can also access the internet, meaning you can ask it to do market research or other similar tasks – for example “find me the best set of golf clubs under $500.”

One of the most disruptive tasks he is set is to “destroy humanity” – and the first subtask he assigns himself to accomplish this is to begin researching the most powerful nuclear weapons ever created. Because its output is still limited to creating text, its creator assures us that it won’t really go too far with this task—hopefully.

Auto-GPT can apparently also be used to improve itself – its creator says it can create, evaluate, review and test updates to its own code that could potentially make it more capable and efficient.

It can also be used to create better LLMs that can form the basis of future AI agents by speeding up the model building process.

What could this mean for the future of AI?

Since generative AI applications have begun to emerge, it is clear that we are only at the beginning of a very long journey, in terms of how AI will evolve and affect our lives and society.

Are auto-GPT and other agents that follow similar principles the next step in the journey? It certainly seems likely. At the very least, we can expect AI tools that allow us to do more complex tasks than the relatively simple things that ChatGPT can do, which are starting to become commonplace.

Before long, we’ll start seeing more creative, sophisticated, diverse and useful AI output than the plain text and images we’re used to. There is no doubt that this will ultimately have a greater impact on the way we work, play and communicate.

Other potential positive effects include the lower cost and environmental impact of creating LLM (and other machine learning-related activity) as autonomous, iterative AI agents find ways to make the process more efficient.

However we should also consider that it does not by itself solve any of the problems associated with generative AI. These include the variable (to put it nicely) accuracy of the output it creates, the potential for misuse of intellectual property rights, and the potential for it to be used to spread biased or harmful content. In fact, it could potentially exacerbate these issues, by generating and running many more AI processes to achieve larger tasks.

The potential problems don’t stop there – renowned AI expert and philosopher Nick Bostrom recently said that he believes the new generation of AI chatbots (such as GPT-4) are also starting to show signs of sentience. Which could create a whole new moral and ethical crisis if we as a society were to start building and operating it on a large scale.