Artificial intelligence (AI) has been evolving rapidly, and one of its most intriguing applications is in the field of natural language processing (NLP). Here, ‘Generative AI’ and ‘ChatGPT’ are noteworthy terms that are helping redefine the ways in which AI interacts with humans.
What is Generative AI?
Generative AI refers to types of artificial intelligence systems that are capable of creating new content autonomously. These AI systems can generate images, music, speech, or text that was not previously existent. They’re ‘generative’ in the sense that they can produce new outputs within the boundaries of their training data.
In NLP, generative models learn to produce text that resembles human-written sentences. They’re trained on extensive corpora of human-generated text, learning patterns, structures, and even the nuances of language during this process. They can then generate text that follows these learned patterns, which can range from simple responses to writing entire articles or even books.
What is ChatGPT?
ChatGPT, developed by OpenAI, is an instance of such generative AI in the field of NLP. GPT stands for ‘Generative Pretrained Transformer’, and it represents a family of models designed for a variety of natural language tasks. The ‘chat’ aspect indicates its design and tuning to engage in conversation with human users, delivering coherent and contextually appropriate responses.
ChatGPT, like its counterparts, has undergone multiple iterations with incremental advancements, such as GPT-1, GPT-2, GPT-3, and as of my knowledge cutoff in September 2021, the upcoming GPT-4.
How was ChatGPT Developed?
The development of ChatGPT involves two main steps: ‘pre-training’ and ‘fine-tuning’. In pre-training, the model is exposed to a vast amount of internet text. However, it’s important to note that the model doesn’t know specifics about which documents were in its training set or access any personal data unless explicitly provided during a conversation.
In this phase, the model learns to predict the next word in a sentence. It is fed chunks of consecutive words from a text and asked to predict the next word. Through this iterative process, the model learns grammar, facts about the world, reasoning abilities, and also unfortunately, biases present in the text it was trained on.
Following pre-training, the model undergoes fine-tuning, a process that trains the model on a narrower dataset with human reviewers following specific guidelines. These reviewers rate potential model outputs for a range of example inputs. Over time, the model generalizes from reviewer feedback to respond more accurately to a wide array of inputs from users.
How Does ChatGPT Work?
The working of ChatGPT can be thought of as a blend of its training process and real-time computation. It takes an input message (or a series of messages), processes this information, and then generates a suitable output.
When you type a message to ChatGPT, it treats the prompt as part of a conversation. The model considers the sequence of words that have been provided, taking into account both the immediate and broader context, and tries to predict the most suitable next word. This process is repeated, choosing one word at a time, until it reaches a specified maximum length or the model generates an end-of-sentence token.
The key to its conversation skills lies in the structure of ‘transformer’ neural networks that it’s built upon. Transformers allow the model to pay varying amounts of ‘attention’ to different words in the input when generating each word in the output, enabling it to maintain context over larger pieces of text.
Generative AI models like ChatGPT represent the leading edge of AI research and development. They hold tremendous potential for a variety of applications, from drafting emails, writing code, creating written content, customer service, tutoring, and more. However, they also come with challenges, particularly related to ethical usage and mitigating biases. As we navigate the new frontier of AI, it is crucial to continuously engage in conversations about these challenges to ensure responsible and equitable AI deployment.