How does ChatGPT work?
Data science has reached unparalleled heights with ChatGPT, a well-liked personal assistant known for its precision in responding to user inquiries. The technology that powers it is far more advanced than it may appear, as it does more than just evaluate each statement and choose the best answer. ChatGPT uses an advanced architecture known as a transformer alongside with deep learning, a subset of machine learning, to produce text that is readable by people.
Let me first clarify these jargons before jumping into ChatGPT.
- Artificial intelligence
- Deep Learning
- Machine Learning
What is Artificial intelligence?
Artificial Intelligence (AI) is a creating intelligent machines capable of performing tasks typically requiring human intelligence. It has a range of technologies, including:
Machine Learning: Enables machines to learn from data without explicit programming.
Deep Learning: It is a branch of machine learning that uses artificial neural networks as its foundation.
Computer Vision: Allows machines to interpret and understand visual information.
Natural Language Processing: Enables machines to communicate and understand human language.
What is Machine learning?
The objective of the artificial intelligence (AI) field of machine learning (ML) is to create systems that can gather information from data. It essentially comes down to teaching computers to "think" like people without having to expressly instruct it to do so.
What is Deep learning?
Artificial neural networks (ANNs) are the main tool used in the artificial intelligence (AI) branch of deep learning for dealing with difficult issues. These artificial neural networks (ANNs) can learn and adapt to new information without needing to be actively programmed, drawing inspiration from the structure and operations of the human brain.
Let me clarify deep learning first, it is like train a computer to learn from experiences, much like how humans learn from experiences. Imagine giving a computer many examples, like photos, and letting it figure out patterns and differences by itself.
A deep learning system can be trained to differentiate between cats and dogs, for instance, by introducing it to several images of each species over time.
It does this by noticing small details and patterns that humans might not even see. This learning process involves layers of calculations and adjustments, kind of like how our brain has layers of neurons.
Deep learning is used in many things we see daily, like when your phone recognizes your face, when a website translates languages, or when a virtual assistant understands your speech. It is all about computers learning and making sense of the world on their own, getting better with more information they receive.
What is Transformer?
Neural network design known as a "Transformer" is used for natural language processing (NLP) applications, including text production, machine translation, and language interpretation.
It is powerful neural network architecture that has revolutionized how we handle sequential data, like text and speech. It's like a skilled translator, able to understand the relationships between words and sentences, and even generate new text that follows those patterns.
What is Neural Network?
A "neural network" is like a smart computer system inspired by the human brain. Neural networks use interconnected nodes to learn from input and make decisions, much like the brain processes information through neurons that are connected.
There are three main components
Input Layer – Its input
Hidden Layer – Its Processing
Output Layer – Its prediction
Let's go back to what is ChatGPT
As name says Chat is just conversation between 2 people and GPT is Generative Pre-trained Transformer. ChatGPT is a chatbot developed by OpenAI and it is conversational artificial intelligence model, this system designed to mimic natural human conversation through text or voice interactions. Consider it your virtual friend that can understand your words, reply intelligently, and even adjust to the direction of the discussion.
Main Component of conversation AI model are as following:
Natural Language Processing (NLP): This technology allows the AI to understand the meaning behind your words, including the context, intent, and even emotions. It employs techniques like text analysis, sentiment analysis, and dialogue management to interpret your input and generate appropriate responses.
Machine Learning (ML): The AI learns and improves over time by analyzing vast amounts of data, including past conversations and user feedback. This allows it to become more accurate and engaging in future interactions.
Foundation models: Large language models, like GPT-3 and LaMDA, serve as the backbone of many conversational AI systems. These models are pre-trained on massive datasets of text and code, enabling them to generate human-like text and understand complex language patterns.
ChatGPT, like other large language models, uses the power of deep learning, specifically a type of neural network architecture called a transformer. Here is a breakdown on how it works internally:
- Pre-training on Large Datasets
ChatGPT has been developed on various kinds of online text - This training involves the model learning language patterns, structures, and various information from a vast corpus of text data.
The core technology behind ChatGPT is the transformer architecture, which is particularly effective in understanding and generating human-like text. This architecture uses mechanisms called attention and self-attention to process and generate language.
When you input text, ChatGPT analyzes it to understand the context and intent. It uses its training to interpret nuances, questions, or instructions in your query.
The model generates a response by predicting the next word in a sequence, considering the input it has received. It does this by calculating the probability of each word in its vocabulary being the appropriate next word and selects the most likely one, continuing this process to build full sentences and paragraphs.
Learning from Interaction
ChatGPT can learn from the interactions within a session. It remembers the context and previous messages in the conversation to provide coherent and contextually relevant responses.
Limitations and Ethical Considerations
- The model has a cutoff in its knowledge, meaning it is not aware of events or developments that occurred after its last training data update.
- Ethical guidelines and safety features are implemented to prevent harmful, biased, or inappropriate responses.
OpenAI continuously works on updating and improving models like ChatGPT, incorporating feedback and new data to enhance performance and accuracy.
Let us go through an example to show how ChatGPT works in a conversation:
"Can you tell me about the history of the Eiffel Tower?"
Understanding the Query: ChatGPT recognizes key terms: "history" and "Eiffel Tower." It understands that the user is asking for historical information about a famous landmark.
Accessing Pre-Trained Knowledge: The model recalls information from its training data related to the Eiffel Tower, including its construction, significance, and historical context.
Generating a Response: ChatGPT starts forming a response, beginning with the most relevant information: "The Eiffel Tower was constructed..." It continues to add details, such as the date of construction, the architect (Gustave Eiffel), its purpose for the 1889 World's Fair, and other historical facts. The model ensures the response is coherent, informative, and directly addresses the user's query.
Presenting the Response: The completed response is presented to the user: "The Eiffel Tower was constructed between 1887 and 1889 as the entrance arch for the 1889 World's Fair. One of the most iconic buildings in the world, the Gustave Eiffel Tower was first rejected for its design by some of the greatest artists and philosophers in France. However, it has since grown to become a global cultural symbol of France."
User Follow-Up Query:
"That's interesting. What is the height of the Eiffel Tower?
ChatGPT's Process for Follow-Up:
Contextual Memory: ChatGPT remembers the previous query was about the Eiffel Tower and understands this is a follow-up question.
Generating a Relevant Response: The model knows from its training that the Eiffel Tower's height is a commonly discussed fact.
It generates a response: "The Eiffel Tower is approximately 324 meters tall, including antennas."
By maintaining context and using its extensive training, ChatGPT provides accurate, relevant, and coherent responses, making it a powerful tool for information retrieval and conversation.
Let us take an example to illustrate how ChatGPT might help in a more complex scenario:
A user is planning a trip to Paris and needs assistance with their itinerary.
"I'm planning a 3-day trip to Paris. Can you suggest an itinerary including major attractions?"
Understanding the Query
ChatGPT identifies key elements: "3-day trip," "Paris," and "itinerary including major attractions." It understands that the user is asking for travel advice with a focus on popular tourist spots.
Accessing Pre-Trained Knowledge: The model recalls information from its training data about Paris, including famous landmarks, cultural sites, and general travel tips.
Generating a Response: ChatGPT structures the itinerary over three days, ensuring a balanced distribution of activities.
Day 1: You may plan to visit the Louvre Museum first thing in the morning, then the Notre Dame Cathedral in the afternoon, and end the day with a stroll down the Seine River.
Day 2: The model could recommend a morning visit to the Eiffel Tower, an afternoon in the Montmartre district exploring Sacré-Cœur and the local art scene, and then an evening at a traditional Parisian café.
Day 3: It might propose a day trip to the Palace of Versailles, with tips on transportation and ticket purchase.
Presenting the Response: The completed response is presented to the user, including suggestions for each day, along with practical tips like best times to visit to avoid crowds, ticket purchasing advice, and transportation options.
User Follow-Up Query:
"That sounds great, but what about food? Can you recommend some French dishes to try?"
ChatGPT's Process for Follow-Up:
Contextual Memory: ChatGPT understands that the follow-up question is related to the Paris trip, specifically focusing on culinary experiences.
Generating a Relevant Response: The model recalls popular French dishes and culinary specialties of Paris. It suggests trying classics like Croissant, Coq au Vin, Ratatouille, Crêpes, and a selection of French cheeses and wines. ChatGPT might also mention famous food districts or markets in Paris, like Le Marais or Rue Cler.
By maintaining context and leveraging its extensive training in various subjects, ChatGPT provides detailed, relevant, and practical advice, making it a valuable tool for planning activities and answering follow-up questions.
With its transformer design and deep learning capabilities, ChatGPT is more than simply a chatbot—it is a window into the future of human-computer interaction. Its capacity for natural, educational, and even humorous dialogues opens the door to a future in which machines coexist peacefully with humans, helping us with work, picking up knowledge from us, and even offering companionship.
The improvements brought forth by ChatGPT and other large language models are obvious, even though there are still drawbacks and moral dilemmas. Their uses will grow as they learn more and develop, with the potential to completely transform industries including the creative, healthcare, education, and customer service.
Are you ready to engage in conversation of the future?
Check out ChatGPT, present it with your most pressing queries, or put it to the test in a creative writing contest. Experience for yourself the strength of this technology and learn about the future opportunities. Keep in mind that ChatGPT is only the beginning of an interactive future.
To see what kinds of conversations you can have, try ChatGPT for yourself.