Generative Pre-trained Transformer 3 also referred to as GPT-3 is the next big revolution in artificial intelligence (AI).
In 2018, a startup, OpenAI was the first to create the autoregressive language model. GPT-3 was deemed to be the largest autoregressive language. The program has been trained regressively on approximately 45 terabytes of text data which has been processed through 175 billion parameters.
These models have known to use a large amount of data from the internet, one of the major reasons that give them the advantage to generate human-like texts. What’s even more interesting is the third version of the GPT model, i.e. GPT-3. Ever since its emergence, the model has created quite a buzz within the developer community.
People started posting tweets mentioning the applications they developed using the GPT-3 API. Though it is still in its beta phase, the API is available once the request is accepted. A great example built using this model was a layout generator. You just need to describe the type of layout you require and the JSX code is developed.
What exactly is Generative Pre-trained Transformer 3?
The GPT-3 program generates texts using algorithms that have already been pre-trained. Wherein the data is already fed into the program, all they need to do is to carry out the specific task.
Generative Pre-trained Transformer 3 is commissioned to be one of the most powerful language models ever created, thanks to the advent of artificial intelligence.
The second model (GPT-2) was released last year where it showed certain convincing streams of text within different ranges of style resulted at the opening of a sentence. However, GPT-3 is a much better version of GPT-2, which is why it is the talk of the town. Undoubtedly the third version of the program has a better AI and this is what makes it significant from the other models. GPT-3 also has a hundred times the database GPT-2 had.
The model shares a unique feature that makes it difficult to differentiate whether the text is written by a human or an AI.
Here’s a sample of how we can use GPT-3 as a writing assistant to further develop sophisticated chatbots.
This is impressive, isn’t it?
Would you be able to identify whether you were on a chat with a chatbot? Perhaps not.
Well, this is what the GPT-3 can achieve.
How does the Generative Pre-trained Transformer 3 model work?
The model tends to generate text one word at a time.
Let’s quote an example…
Imagine the developer gives the following words as input.
“If today is my turn then might be tomorrow will”
The given artificial intelligence model could generate the word “be” as the response. Further on the developer catches the generated word to the input and reruns the model.
“If today is my turn then might be tomorrow will be yours”
At this point, the model can try generating the word “yours” as the response. Repeat the same process, and the third should be the “period” sign, therefore getting the complete sentence.
“If today is my turn then might be tomorrow will be yours.”
This is likely possible using GPT-3. How? The major reason behind this is because the model is already aware of a particular pop culture reference multiple times from texts received during rigorous training. Therefore, neural networks do most of the work by guessing the next word to pop up offering high degrees of certainty.
Generative Pre-trained Transformer 3: The functionality
Created by the startup OpenAI, San Francisco, GPT-3 is a gigantic neural network and is a part of a segment of deep learning in machine learning. The model is a great achievement in the field of AI.
GPT-3 is an example of what’s known to be the language model which is partly a type of a statistical program.
GPT-3 is an acronym that signifies “generative pre-training.” Well known as generative because of the long sequence of original texts it produces as the output, unlike the other neural networks that answer only with a yes or a no.
The model does not have a domain knowledge built within, which is why it is called pre-trained despite having the capabilities of completing domain-specific tasks i.e. foreign language translation.
The Generative Pre-trained Transformer 3 model is a program that estimates how likely a single word would appear in the given incomplete sentence, also called conditional probability of words. Uncanny as it seems, it is all because of artificial intelligence.
When a neural network gets developed, it is called the training phase. During this phase, the model is fed with millions of text samples which get converted into words called numeric representations and vectors. Now, this is called data compression. Further on, these compressed texts are unpacked into valid sentences. Eventually, the process of compressing and decompressing helps in developing the program’s accuracy to calculate the conditional probability of words to be used in the sentence.
Once the model is trained, it gets the ability to predict what words need to come next when a person writes the first few words. This action of predicting the next word is called “inference” in machine learning.
Not only does the model get to know which words are likely to appear but also forms a genre or a type of written task. For example, if the GTP-3 is fed with names of poets along with their work samples, then it predicts the name of another poet who has similar rhythm and syntax to the poet whose name has been fed.
What else can GTP-3 do? It can spin stories for the children’s storybook, completes the code, writes tweets, and headlines. Most important of all, it can easily be used to query databases.
In a nutshell
Generative Pre-trained Transformer 3 is here to shape the AI’s future. However, in the end, it all depends upon the user of the technology. With innovation in our hands, it is for us to decide what impact will technology have in our lives.