Like many people, you may have recently been excited about the possibility of ChatGPT and other big language models like the new Bing or Google’s Bard.
For anyone who somehow hasn’t come across them, which is unlikely since ChatGPT is said to be the fastest growing app of all time, here’s a quick summary:
Large language models (LLM) are software algorithms trained on huge text data sets, allowing them to understand and respond to human language in a very realistic way.
The most famous example is ChatGPT, a chat interface powered by GPT-4 LLM, which suddenly captured the attention of the whole world. ChatGPT is able to speak like a human and generate everything from blog posts, letters and emails to prose, fiction, poetry and even computer code.
As impressive as they are, until now, LLMs have been limited in one significant way: they are usually only able to complete a single task, such as answering a question or generating text, before requiring more human interaction (known as prompt- these).
This means they aren’t always great at more complicated tasks that require multi-step instructions or depend on external variables.
That’s where he comes into play Auto-GPT – a technology that tries to overcome this obstacle with a simple solution. Some believe it could even be the next step towards the Holy Grail of artificial intelligence – the creation of general or strong artificial intelligence, General AI.
Let’s first look at what this means:
Strong AI vs Weak AI
Current AI applications are typically designed to perform a single task, getting better at it as they acquire more data. Some examples include image analysis, language translation or autonomous vehicle navigation. This is why they are sometimes called specialized AI or weak AI.
A generalized AI is one that is theoretically capable of performing many different types of tasks, even those for which it was not originally created, in the same way that a naturally intelligent entity (such as a human) can. Sometimes it is called strong AI or artificial general intelligence – AGI (artificial general intelligence).
AGI may be what we traditionally thought of when we envisioned what AI would look like in the days before machine learning and deep learning made weak AI commonplace around the turn of the last decade. Imagine sci-fi AI demonstrated by robots like the Star Trek character Data, who can do almost anything a human being can do.
So what is Auto-GPT?
The simplest way to explain it is that Auto-GPT is able to perform more complex multi-step procedures than existing LLM applications by creating its own queries and returning them to itself, creating a loop.
Here’s one way to think about it: getting the best results from an app like ChatGPT requires careful thought about how you phrase the questions you ask it. However, why not let the app ask the question itself? And while on that step, also ask the question of what the next step should be, and how it should be solved, and so on in a circle, creating a loop until the task is completed.
It actually works by breaking a larger task into smaller subtasks and then extracting independent Auto-GPT instances to work on them. The original instance acts as a kind of project manager, coordinating all the work done and compiling it into a finished result.
In addition to using GPT-4 to construct sentences based on the text it has studied, Auto-GPT is capable of searching the Internet and incorporating the information it finds there into its calculations and output. In this respect, it is more similar to the new GPT-4 enabled version of Microsoft’s Bing browser. It also has better memory than ChatGPT, so it can construct and remember longer chains of commands.
Auto-GPT is an open source application that uses GPT-4 and was created by Toran Bruce Richards. It’s Richards said that he was inspired to develop Auto-GPT 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 called recursive AI agents because they have the ability to autonomously use the results they generate to create new queries, chaining these operations together to complete complex tasks.
What are some of the applications of Auto-GPT and AI agents?
While applications such as ChatGPT have become famous for their ability to generate code, they are usually limited to relatively short and simple programming and software design. Auto-GPT, and potentially other AI agents that work in a similar way, can be used to develop software applications from start to finish.
Auto-GPT can also help businesses independently increase their net worth by examining their processes and providing intelligent recommendations and insights on how they can be improved.
Unlike ChatGPT, it can also access the Internet, which means you can ask it to conduct market research or perform other similar tasks — like “find me the best set of golf clubs for under $500.”
One extremely disruptive task he has been given is to “destroy humanity” – and the first sub-task he assigned himself to do was to begin research into the most powerful atomic weapon of all time. Since its output is still limited to text creation, its creator assures us that it won’t actually get very far with this task – hopefully.
Auto-GPT can also improve itself – its creator says it can create, evaluate, review and test updates to its own code that can potentially make it more capable and efficient.
It can even be used to create better LLMs that could form the basis of future AI agents, by speeding up the model creation process.
What could this mean for the future of AI?
Ever since generative AI applications began to emerge, it was clear that we were only at the beginning of a very long journey in terms of how AI would evolve and impact our lives and society.
Are Auto-GPT and other agents following the same principles the next step on that path? It certainly seems that way. At the very least, we can expect AI tools that allow us to perform far more complex tasks than the relatively simple things ChatGPT can do to begin to become commonplace.
Soon we will start to see more creative, sophisticated, diverse and useful AI output than the simple text and images we are used to. This will no doubt end up having an even greater impact on the way we work, play and communicate.
Other potential positive impacts include reducing the cost and environmental impact of LLM creation (and other machine learning-related activities) as autonomous, recursive AI agents find ways to make the process more efficient.
However, we must also consider that it does not in itself solve any of the problems associated with generative artificial intelligence. These include the variable (to put it mildly) accuracy of the results it generates, the potential for intellectual property rights to be abused, and the potential for it to be used to spread biased or harmful content. In fact, by generating and running many more AI processes to accomplish larger tasks, it could potentially increase these problems.
The potential problems don’t stop there – eminent artificial intelligence expert and philosopher Nick Bostrom is recently said to believe that the latest generation of AI chatbots (such as GPT-4) are even starting to show signs of awareness. Which could create a whole new moral and ethical dilemma if we as a society plan to start creating and operationalizing them on a large scale.
Source: PC Press by pcpress.rs.
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