Introduction: What is ChatGPT?

In recent years, there has been rapid advancement in the field of Artificial Intelligence, and one of the latest developments is Chat-GPT, an AI chatbot, created and launched by Open-AI in November 2022. It is trained to interact in a conversational way and has set the record for the fastest-growing user base. In just two months after launch, it was estimated to have reached 100 million monthly users with an average of 13 million unique visitors per day.

In this article, I’m going to explain what Chat-GPT is and how it works in the most basic of terms.

What is the Technology Behind ChatGPT?

Think of how in cartoons, we have this really smart, friendly robot that knows a lot and interacts in the most human-like way possible. Chat-GPT is a chatbot similar to this friendly robot. It generates human-like texts based on information provided (prompts). You ask it a question or give it a code to debug, and you get a very human-like and easy-to-understand response. Now let me explain to you how it works and the technology behind it.

Chat-GPT uses a unique type of technology called Artificial intelligence, or AI for short. This is just like giving a computer a brain so it can think and learn. Just like humans, computers can also learn new things and get better at them with the help of humans who teach them.

The brain Chat-GPT uses is called GPT-3.5, which is what we’re more interested in, 3.5 is just the version number. GPT stands for Generative Pre-trained Transformer which is just a fancy term for a language model that is trained to generate texts. GPT has been pre-trained on over 40 GB of text data, which has allowed it to learn a wide range of general features that can be used in various tasks. It uses a neural network with transformer architecture and deep learning techniques to generate text. Don’t panic, I’ll break that down for you.

How it Works

1. Neural Network: This is an algorithm modeled after the human brain. The human brain has lots of interconnected cells known as “neurons” that are responsible for carrying information throughout the human body and are considered the basic unit of the nervous system. They form an incredibly complex network of communication.
   
   Just like the human brain, a neural network has a lot of interconnected parts arranged in layers called nodes or neurons that work together to help computers figure things out. They can learn and improve as they are used, which improves their accuracy over time. This is referred to as Training and would be discussed next.
2. Deep Learning: Just like training an animal on a particular skill or behavior or teaching a child the alphabet, deep learning is like teaching a computer to learn things by itself over time. I’ll explain.

A neural network can learn when shown lots of examples, from simple to complex. For instance, they can be trained to recognize a ball. It is fed lots of examples, and it tries to figure out what they all have in common. The first layer is taught to recognize basic lines and shapes, in this case, a round shape. The next layer learns to recognize more complex properties, like color. The next layer learns to recognize texture. In the final layer, it pulls all these patterns together to recognize the image of a ball.

This process is iterative, in the sense that it is repeated as many times as possible until the algorithm produces a satisfactory level of accuracy. Once this has been achieved, it can now recognize new images of a ball on its own, just like you can recognize a ball when you see one.

3. Transformer Architecture: This is designed to understand the context of an input where it processes and transforms a sequence of data, e.g., sentences, to generate an intelligible response.

This is how it works:

• Encoding: Assume you ask Chat-GPT “I want to learn how to bake a cake”, It first does what is known as Tokenization. This simply means assigning mathematical values to these words because, unlike humans, transformers do not understand words.

Next, these words are assigned weights based on how important they are to help the transformer understand the context of the sentence. This is known as Self-Attention. It breaks the input down into fewer words, i.e. “How bake cake”. This helps the model work out the context o the prompt.

The self-attention output is then passed to the Feed-Forward network which applies more transformations to the output by applying a series of mathematical operations to help the transformer understand the relationships between the words even better.

• Decoding: This stage is crucial in the response generation process. The transformer now uses what it learned to generate a meaningful response based on the input and predicts what words will come next based on previously predicted words. It does not stop generating words until it thinks it has completed the response to the input, puts them all together, and generates the final output we see.

CONCLUSION

Chat-GPT is our friendly chatbot that uses deep learning to understand and respond to human language. Some people may worry that Chat-GPT will replace human workers, but the reality is that Chat-GPT is here to improve human capabilities, not replace them. Chat-GPT can help us become more productive and efficient by handling routine tasks, which in turn allows us more time to focus on more complex work.

For example, Chat-GPT can be used by content writers/creators to generate blog posts or content ideas and overcome creative’s block. Companies can also use Chat-GPT for customer service to handle customer inquiries and complaints 24/7. This would improve customer satisfaction by reducing wait times and providing assistance even outside of work hours.

While it is a powerful tool for increasing productivity and efficiency, we should not rule out the fact that it can be used for malicious purposes like cheating and fraud.

I hope you have been able to get a glimpse of the “magic” that is Chat-GPT.

In my next article, I will be writing about add-ons for functionality that can enhance Chat-GPT beyond its current capabilities.

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