How does ChatGPT work? Here’s a deep dive into what makes the conversational AI chatbot tick and talk.
Since its launch in November 2022, ChatGPT has become an internet sensation. Within the first five days, there were roughly 1 million user registrations. That crazy number broke some records and blew some minds.
This question-answering Artificial Intelligence (AI) solution has demonstrated an acceleration of human-machine collaboration.
Whether you’re stuck on a math problem, experiencing writer’s block, or digging into history, this AI chatbot claims to know it all. For example, it can write fictional stories, essays, blog posts, application letters, and even basic code for software startups.
And, among other features, ChatGPT remembers previous conversations and declines inappropriate requests.
In this post, we’ll discuss the architecture, working, applications, and limitations of ChatGPT.
What’s ChatGPT?
ChatGPT is a large language model from GPT-3.5 by OpenAI. This AI-based chatbot interacts in human-like conversational dialogues to answer follow-up questions and commands.
ChatGPT uses modern AI capabilities to generate responses to human queries. It generates human-like text using deep learning techniques.
By deep learning, we mean the technique that any machine uses to act like a human. It’s the first step to achieving artificial intelligence, where humans can feed the machine filtered, unfiltered, or semi filtered data to get the desired result.
The model learns from a massive dataset of internet text, giving it a broad understanding of various topics.
You can configure it to perform different language tasks – language translation, question answering, and text summarization, for example.
It knows diverse fields, making it a powerful tool for NLP applications like chatbots, advertising copywriting, e-commerce strategy, sales funnel builders, and more.
How Does ChatGPT Work?
ChatGPT is a Transformer neural network language model.
Transformer is the name of the architecture. Before its name change to Inferkit, the popular Talk To Transformer AI text-generation tool from 2019 took its name from this architecture.
ChatGPT uses a large dataset of internet text for training, which helps it learn language patterns and the relationships between words.
It then uses this knowledge to generate new text similar to the training data.
Basically, it’s a deep learning algorithm that calls the attention mechanism to generate text. In other words, the machine tries to mimic the attention of humans in its neural networks.
The Model Training Method
ChatGPT uses unsupervised learning for training. This means the model lacks explicit instructions or labels on generating text.
So, ChatGPT gets no instructions from its creators until it pumps out the result. It works on very minimal feedback.
That doesn’t mean that the data isn’t labeled. Instead, it comes from a large, established dataset of internet text, allowing it to learn language patterns and the relationships between words.
The training process begins by preprocessing the dataset. The model then uses the patterns it learns from the training data to generate new text one word at a time.
The AI-based generated text is compared to the true text, or literal text. It’s kind of like how robots speak in movies from the 1980s. The way we speak is “human speak” with fillers, cliches, idioms etc., but robots speak in chopped up text as if they were reading it verbatim.
So, the model’s parameters adjust to minimize the difference between the generated text and the true text.
The model also uses a technique called teacher forcing during training, where the correct next word is provided as input during training; this way, the model learns how it can predict the next word more accurately.
Once the model absorbs a large dataset, it further fine tunes itself on a specific task by training on a smaller dataset.
The model gets a more detailed understanding of the task and the language used in the domain. It can generate text that is accurate and relevant to the task.
Inner Workings of ChatGPT
We’re going to get a bit technical here, so strap in.
As stated above, ChatGPT is built on the Transformer architecture. This allows it to handle long-term dependencies in the text and generate a more coherent and contextually accurate output.
When the model generates text, it starts with an initial input, like a prompt or a partial sentence. The input passes through an embedding layer, which converts the words into a dense vector representation.
These vectors are processed by the model’s encoder layers, which use self-attention mechanisms to understand input context.
The output of the encoder layers passes to the decoder layers, which generates the output text one word at a time. The decoder then uses its internal state, including the patterns it picks up from the training data, to create a new text.
The decoder also uses an attention mechanism to decide which words in the input are most relevant to the current word it is generating.
Overall, ChatGPT uses transformer architecture, attention mechanisms, and deep learning algorithms to generate text that is similar to the text it was trained on.
Additionally, it can be fine-tuned for specific tasks to generate more accurate and contextually relevant output.
Applications of ChatGPT
ChatGPT is an intelligent technology model with invincible capacities. It can be your advisor, tutor, or companion, no matter where you are.
Some of the many applications of ChatGPT include:
- speech and text analysis
- translations
- explanations of complex issues
- writing stories and essays
- generate funny content/telling jokes
- working as a virtual cloud
- compose marketing plans
- assignment and homework help
- hotel recommendation
- recipe suggestions
- blogging
- learn to code
- debugging code
- SQL queries building
- customer assistance
- resume writing
- relationship advisory
- job interview preparation
All of this is just the tip of the iceberg, though. ChatGPT’s power structure has way more to offer.
ChatGPT Limitations
ChatGPT understands the inputs given by humans using the Reinforcement Learning model. Its response directly depends on the quality of input the user gives.
However, like all other technologies, ChatGPT does come with some limitations.
Its weaknesses are more prominent and, therefore, avoidable. Even the ChatGPT interface lists three limitations which clearly shows that there is room for improvement. It’s obvious the developers are well aware.
Some ChatGPT weaknesses that have been discovered so far include:
- ChatGPT can produce incorrect answers to user queries. It does not always guarantee correct information.
- You can’t use ChatGPT to write blog posts or research papers. The text is detectable using some online tools.
- It can generate biased suggestions, which may lead to dangerous or violent behavior in users.
- It can be easily fooled by imitation to get age-inappropriate responses.
- If it doesn’t understand the question, it may answer by guessing what the user intends to ask.
- It sometimes rephrases or restructures questions to respond to a query.
- It restricts user input to text and doesn’t allow uploading data beyond it in other formats
- The major drawback is that its data comes from 2021, with no knowledge of events after that.
Despite the limitations, ChatGPT is an incredible tool for natural language processing tasks because of its ability to understand and generate human-like responses.
It has the potential to substantially change education, healthcare, aviation, and other sectors. Additionally, ChatGPT is expected to generate up to $1 billion in revenue by 2024.
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Author Bio:
Aziz Khan is a Solutions Architect at Summa Linguae Technology specializing in delivering profitable solutions for clients.
When Aziz is not working, he spends time in the gym, researching technology, and working on his blog.
